Abstract
BackgroundThere exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems, such as PubMed, offer flexible query interface, but churn out a list of matching documents that users have to go through the results in order to find the answers to their queries. On the other hand, the "deep" search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to alleviate these problems, we introduce a new search engine, BOSS, Biomedical Object Search System.MethodsUnlike the conventional search systems, BOSS indexes segments, rather than documents. A segment refers to a Maximal Coherent Semantic Unit (MCSU) such as phrase, clause or sentence that is semantically coherent in the given context (e.g., biomedical objects or their relations). For a user query, BOSS finds all matching segments, identifies the objects appearing in those segments, and aggregates the segments for each object. Finally, it returns the ranked list of the objects along with their matching segments.ResultsThe working prototype of BOSS is available at http://boss.korea.ac.kr. The current version of BOSS has indexed abstracts of more than 20 million articles published during last 16 years from 1996 to 2011 across all science disciplines.ConclusionBOSS fills the gap between either ends of the spectrum by allowing users to pose context-free queries and by returning a structured set of results. Furthermore, BOSS exhibits the characteristic of good scalability, just as with conventional document search engines, because it is designed to use a standard document-indexing model with minimal modifications. Considering the features, BOSS notches up the technological level of traditional solutions for search on biomedical information.
Highlights
There exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific “deep” search systems
There exist many special-purpose deep search systems that provide information pre-extracted from academic references, such as PPI-finder [7] for protein-protein interactions, and STRING [8] and iHOP [9] for protein networks
BOSS is designed to fill the gap between the two opposite ends of the spectrum: general-purpose and domain-specific deep search systems
Summary
There exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific “deep” search systems. The “deep” search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, are often found only within some predefined contexts. Pinpointing relevant information has become an extremely labor-intensive and time-consuming process To address this problem, researchers have introduced search services especially concerning academic literature. Google Scholar [2] and Microsoft Academic Search [3] are well known examples These are general-purpose academic search engines covering all topics. PubMed [4] is another well known example tailored for biomedical disciplines These search engines serve as a good entry point for researchers, they produce relevant article lists only, leaving most of the information-processing task to users. It is the user’s job to read through the articles and manually compile the answer to the query
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